Search

Abstract: Satellite observation in East Asia is important because aerosols of this region are very complex, not only from anthropogenic but also from natural sources, which have been recognized as a major source of regional and global air pollution. However, retrieving aerosols properties over land is difficult because of surface reflection, complex aerosol composition, and aerosol absorption. In this study, a new aerosol retrieval method including two steps was developed to invert AOT and aerosol type using PARASOL TOA measurements. Both reflectance (490nm) and polarized reflectance (670nm and 865nm) are used in the algorithm to improve the accuracy of retrieved AOT and aerosol type. AOT was determined by comparing the aerosol reflectance and polarized reflectance with pre-calculated LUTs of six aerosols clustered from the inversion products of AERONET sites in East Asia. The comparison between PARASOL and AERONET showed improvement in total AOT using the suggested methodology. Three case studies show inverted aerosol AOT and types agreed well with specific aerosol types. [Copyright &y& Elsevier]

Stripe noise is considered one of the largest issues in space-borne remote sensing. The features of stripe noise in high-resolution remote sensing images are varied in different spatiotemporal conditions, leading to limited detection capability. In this study, we proposed a new detection algorithm (LSND: a linear stripe noise detection algorithm) considering stripe noise as a typical linear target. A large-scale stripe noise dataset for remote sensing images was created through linear transformations, and the target recognition of stripe noise was performed using deep convolutional neural networks. The experimental results showed that for sub-meter high-resolution remote sensing images such as GF-2 (GaoFen-2), our model achieved a precision of 98.7%, recall of 93.8%, F1-score of 96.1%, AP of 92.1%, and FPS of 35.71 for high resolution remote sensing images. Furthermore, our model exceeded ~40% on the accuracy and ~20% on the speed of the general models. Stripe noise detection would be helpful to detect the qualities of space-borne remote sensing and improve the quality of the images. [ABSTRACT FROM AUTHOR]

Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms. [ABSTRACT FROM AUTHOR]